Food Safety and Diversity: Knowledge Transfer/Translation and Chinese Newcomers in Toronto
Bibliographic record
Abstract
Canada with its growing immigrant population (19.8 % of the total population born outside of Canada in 2006) is home to over 200 ethnic communities. This cultural, ethnic and linguistic diversity, while enriching the Canadian society, presents a significant challenge to public health organizations, in determining the diverse needs of these communities, breaking linguistic barriers and ensuring newcomers’ access to information and public services. To ensure the safety of traditional foods and facilitate safe food handling, the educational and information needs of community members as well as the service providers themselves (public health nurses and inspectors, community workers and immigrant settlement workers) need to be addressed. However there are wide knowledge gaps in the area of traditional foods and food safety as identified by a background report prepared by the Public Health Agency of Canada - this includes information on traditional foods, food handling and preparation practices, educational needs of public health officials and newcomers. Partnerships between communities, food safety professionals, practitioners and academics can create mutual learning opportunities and facilitate exchange of knowledge and experience to bridge these gaps resulting in an increased understanding of traditional foods and food handling practices. Immigrants from China represent one of the largest groups of recent arrivals to Canada. This project provides a community perspective concerning food safety as expressed by recent Chinese immigrants, and how this knowledge can be transferred to local health officials. This study explores the food safety practices among the recent Chinese immigrants in the Greater Toronto Area, and the educational and informational needs as well as the knowledge gaps between the recent Chinese immigrants and their health service providers (public health officials/inspectors, community workers and health educators). By using mixed research methods of collecting data through a small-scale survey, focus group, and individual interviews, this study provides insights on food safety as expressed by recent Chinese immigrants, one of the largest immigrant groups to Canada in the past decade, and by the local health officials and community workers in order to find more effective ways to facilitate knowledge exchange/transfer among and between the two groups. Prepared for Public Health Agency of Canada (PHAC).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".